126 citations · 472 across the 48 of their papers we have counts for
48 papers
GLoRI: Closed-Loop Whole-Body Tracking with Global-Local Reference Interaction for Humanoid Loco-Manipulation
Qingyao Xu, Sheng Yin, Zibo Zhou +3
Humanoid loco-manipulation requires accurate whole-body motion tracking in the world frame for physical interaction. While local references preserve motion structure, they lack exp…
Neural Message Passing for Visual Relationship Detection
Yue Hu, Siheng Chen, Xu Chen +2
Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and…
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Ulrich Neumann +3
Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent…
Spatio-Temporal Graph Complementary Scattering Networks
Zida Cheng, Siheng Chen, Ya Zhang
Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition. To achieve effective analysis, spatio-t…
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer
Jinxiang Liu, Yangheng Zhao, Siheng Chen +1
Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes imp…
Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction
Maosen Li, Siheng Chen, Yangheng Zhao +3
We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GN…
CaT: Weakly Supervised Object Detection with Category Transfer
Tianyue Cao, Lianyu Du, Xiaoyun Zhang +3
A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fu…
Cooperative Learning for Noisy Supervision
Hao Wu, Jiangchao Yao, Ya Zhang +1
Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empirically disclosed that utilizing dual networks can enhance the…
MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
Shixiang Feng, Yuhang Zhou, Xiaoman Zhang +2
Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper invest…
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning
Zida Cheng, Siheng Chen, Ya Zhang
3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D la…
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Maosen Li, Siheng Chen, Yanning Shen +3
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative predictio…
A Fourier-based Framework for Domain Generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang +2
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data. Domain generalization aims at tackl…
Collaborative Label Correction via Entropy Thresholding
Hao Wu, Jiangchao Yao, Jiajie Wang +3
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. W…
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
Chen Ju, Peisen Zhao, Siheng Chen +3
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incomp…
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks
Mingming Lu, Ya Zhang
Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversa…
Uncertainty-aware Incremental Learning for Multi-organ Segmentation
Yuhang Zhou, Xiaoman Zhang, Shixiang Feng +2
Most existing approaches to train a unified multi-organ segmentation model from several single-organ datasets require simultaneously access multiple datasets during training. In th…
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Chenxin Xu, Siheng Chen, Maosen Li +1
We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learnin…
Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses
Chen Ju, Peisen Zhao, Ya Zhang +2
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the…
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Fei Ye, Huangjie Zheng, Chaoqin Huang +1
Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consisten…
Privileged Knowledge Distillation for Online Action Detection
Peisen Zhao, Lingxi Xie, Ya Zhang +2
Online Action Detection (OAD) in videos is proposed as a per-frame labeling task to address the real-time prediction tasks that can only obtain the previous and current video frame…
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
Chaoqin Huang, Fei Ye, Peisen Zhao +3
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new…
Learning on Attribute-Missing Graphs
Xu Chen, Siheng Chen, Jiangchao Yao +3
Graphs with complete node attributes have been widely explored recently. While in practice, there is a graph where attributes of only partial nodes could be available and those of…
Sampling and Recovery of Graph Signals based on Graph Neural Networks
Siheng Chen, Maosen Li, Ya Zhang
We propose interpretable graph neural networks for sampling and recovery of graph signals, respectively. To take informative measurements, we propose a new graph neural sampling mo…